The present invention relates generally to apparatus and methods for measuring vegetative mass and the mass of fruit on plants such as, but not limited to, grapevines, greenhouse tomatoes, and cane berries, which are typically supported on a trellis structure.
Worldwide, grapes are the most widely-planted fruit crop, and rank third in overall tonnage produced. Grapes, particularly those which are destined for juice or wine, are quite perishable once picked. Hence, processing cannot be delayed and must generally be accomplished immediately after harvest.
Crop yield varies from year to year, a fact that can have a significant bearing on processing because of the need for harvesting equipment, farm laborers, and other infrastructure. Hence, juice processors and wineries need the ability to forecast yield as accurately as possible in order to arrange for efficient harvesting, processing, fermentation, and storage.
Moreover, grape and fruit quality are also affected by overall yield, and optimum quality can be achieved by thinning a crop at some point prior to its harvest. It is therefore advantageous to be able to predict yield well in advance of harvest in order to permit thinning.
The current method of estimating yield in grapevines is labor-intensive and is not particularly accurate. The standard technique currently in use essentially involves collecting fruit samples by hand from particular plants in the field and comparing these samples to past years' data. This hand sampling is highly labor-intensive. From indicator vines sampled annually or from randomly selected vines, the average number of clusters per grape vine is determined; and after fruit set, the average number of berries per cluster is calculated. Once or twice thereafter, either whole fruit cluster or individual berry samples are collected and the average weight per cluster or berry is computed. These weights are compared with those from previous years that are considered to be biologically and meteorologically similar to the current season. Final berry or cluster weight, which is translated into “yield,” is projected using a ratio of the current sample weight to a comparison year's weight at that sampling date. Unless samples are collected more frequently, the grower does not have an indication of the dynamics of berry growth, which change with variety and season at a given site.
There are guidelines for establishing adequate sample sizes, but the cost and time involved in hand sampling impose practical limits on the size of the dataset. Self-reporting suggests that industry-wide, the accuracy of estimating final yield is 10%, with larger, older operations achieving the best results in large part due to extensive historical databases of cluster and berry weights.
Savings in time and labor could be realized if inexpensive equipment were developed to automate crop estimation within the accuracy of the current method. Moreover, yield estimates and decisions about crop thinning could be improved through the use of continuous data collection on crop mass during fruit development.
Hence, a need exists for new automated systems for weighing trellised crops and/or estimating crop yields of trellised crops, such as grapevines.
One manner of automating this process is to take advantage of the fact that as plants on trellises grow, the load which is borne by the trellis increases. More particularly, increasing plant mass which is supported by a trellis' horizontal support wire operates to increase the tension in the wire itself, some of which tension is transferred to the trellis posts. Loads on posts in orchards have in fact been measured for the purpose of designing sufficiently robust trellises, but measurements of wire tension in trellises have not been previously utilized for estimating crop yield.